Quantitative Point-of-Care Colorimetric Assay Modeling Using a Handheld Colorimeter
Bibliographic record
Abstract
Colorimetric assays typically offer a rapid and convenient method to assess analytes that span healthcare monitoring to water quality testing. However, such tests can only provide qualitative results when employed in resource-limited settings or require bulky and expensive equipment such as lab spectrophotometers to allow quantitative measurements. In this paper, we report on the use of a handheld colorimeter to quantitatively determine the concentration of analytes in a manner that is independent of ambient lighting or initial sample color. The method combines the response of the sensor with first-principles modeling that better describes the nature of the assay compared to linear-in-parameters regression modeling that is typically performed in other studies. This method was successfully demonstrated using a number of colorimetric assays: (1) determination of solution pH using a universal indicator, (2) quantification of the DNase presence using a DNA-gold nanoparticle assay, and (3) quantification of the concentration of the antibiotic tetracycline using a cell-based assay.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".